Insights on AI inference, ASIC infrastructure, and building fast AI applications.
Quantization shrinks model weights from 16-bit to 4-bit or 8-bit, cutting memory usage and speeding up inference. Here's how the major techniques work and when to use each one.
MQA and GQA reduce the memory footprint of attention by sharing key-value heads across queries. A simple architectural change that makes inference dramatically faster.
Before continuous batching, LLM servers wasted GPU cycles waiting for the slowest request in each batch. Orca's iteration-level scheduling fixed this with a 36x throughput improvement.
The original speculative decoding papers needed a separate draft model. Medusa, EAGLE, and Sequoia found ways to speculate faster, smarter, and without the extra model.
SGLang's RadixAttention stores KV cache in a radix tree, enabling automatic prefix sharing across requests. The result is up to 5x higher throughput for multi-turn and structured workloads.
Speculative decoding uses a small draft model to predict multiple tokens ahead, then verifies them all at once. The result is mathematically identical output, 2-3x faster.
The PagedAttention paper solved the biggest memory waste problem in LLM serving by borrowing an idea from operating systems. Here's how it works and why vLLM became the default serving framework.
FlashAttention rewrote the rules of transformer inference by treating attention as a memory problem, not a compute problem. Here's how it works and why it matters.
A step-by-step tutorial for building a voice AI agent with sub-500ms response times. Plus: why General Compute is the only provider fast enough to use reasoning models in a voice pipeline.
Coding agents make dozens of sequential LLM calls per task. Every millisecond of inference latency compounds across each step, making speed the single biggest infrastructure bottleneck for AI-powered developer tools.
Model quality has commoditized. The real competitive advantage in AI is how fast your infrastructure can deliver results. Inference speed is becoming the defining moat for AI-native products.